It's specifically referring to these forecast models having accuracy about on par with the state-of-the-science global numerical weather forecast models. "Accuracy" here specifically means esoteric metrics like the "500mb anomaly correlation coefficient" (basically a summary statistic that tells you how well the 3D atmosphere fields predicted by the model match what we observe later on).
This entire class of global numerical weather forecast models has had more-or-less a monotonic increase in forecast accuracy over the past five decades. E.g., a 72 hour forecast from the current generation of these models has similar error statistics to a 24 hour forecast from its predecessors in the early 2000's.
What's special about these AI forecast models is that they are significantly cheaper to run than the existing global numerical weather forecast models. Modern meteorology involves a great deal of statistical analysis to overcome chaotic uncertainty. One way we build these statistical analyses is to run dozens of forecasts with the same model, using slightly different initial conditions, to see how the forecasts diverge. But these ensembles are very under-disperse - a few dozen members just doesn't fully sample the uncertainty. Now, if you can run 1000x the number of ensemble members, a whole new world of possibility opens up.
And that's before you consider just training the AI system to directly output a posterior distribution representing this uncertainty in the first place...